Demos-Nothing-To-Fear-But-Fear-Itself

1 Great Britain 2

1 Great Britain 2 Original analysis on the drivers of Brexit Introduction As part 1 demonstrates, there is already a significant amount of evidence available on the demographic, economic, geographical and attitudinal breakdown of the Brexit vote. Our original analysis has two aims. First, we wanted to refine the existing evidence by combining individual demographic information with a place-based analysis. Existing analysis shows the kinds of areas more likely to vote to leave the EU: rural areas and smaller towns, more deprived areas, and areas that have experienced a rapid increase in immigration. We wanted to see whether there is an environmental effect of living in these areas separate to individual demographic trends. Second, we wanted to explore key social and political attitudes through the lens of a new political divide that has been termed ‘open versus closed’ outlooks. There is much evidence to suggest the Brexit vote and other recent international political events have in part been driven by a reaction to globalisation, trade, immigration and a perceived lack of ‘control’ over voters’ lives and communities. If this is the case, we hypothesise that demographic and place-based factors are an important predictor of voters’ attitudes and political behaviour, and their everyday experiences and interactions with the wider world outside their immediate geographic community inform their views, and ultimately their political preferences. This is tangentially related to the ‘contact hypothesis’, which states that – under certain conditions – contact between members of different groups can reduce prejudice and inter-group conflict. 88 However, we wanted to test whether broader social circles in a geographic sense alone were sufficient to change social and political attitudes.

75 As part of this project, we commissioned YouGov to conduct surveys of voters in six European countries, and analysis presented in this chapter uses the results of the British survey. 89 These included questions about attitudes to the EU, ethnic and religious diversity, globalisation, international cooperation, and political and social trust. With the aim of testing the above hypothesis, we asked an additional set of survey questions, to measure: ·· the geographical extent of respondents’ social networks – whether they regularly socialise with people who live outside their local area, in different parts of Britain, or in other countries ·· respondents’ long-term geographic mobility – whether they have they lived in the same town their whole life, in different parts of Britain or even different countries ·· respondents’ short-term geographic mobility – whether they have they travelled abroad in the last 12 months Method Geographic mobility and social networks are likely to be partially correlated with socioeconomic factors. Similarly, we wanted to be able to disentangle individual demographics and place-based factors: residents in more deprived areas or those who have experienced more immigration might be more or less likely to support globalisation or the EU, but is this because of where they live, or simply because these places are inhabited by a higher proportion of people with individual traits (eg education or income) that are associated with certain attitudes or behaviours? To disentangle these overlapping factors, we employ logistic regression analysis. This involves building a model of all the factors we reasonably believe to affect a given outcome such as an individual voting to leave in the EU referendum, or saying they think globalisation has had a negative effect on their life. We are then able to pick out a single factor, or explanatory variable, and measure its ‘average marginal effect’ on the outcome of interest.